Wednesday, January 19, 2011

New Learning Theory #cck11

I have just started this course and am getting my head around this new learning theory...I have underlined the points from George Siemens' paper that most strike a cord.

According to this, I am pretty smart! -Jennie (#cck11)

"The pipe is more important than the content within the pipe. Our ability to learn what we need for tomorrow is more important than what we know today" George Siemens


Connectivism is the integration of principles explored by chaos, network, and complexity and self-organization theories. Learning is a process that occurs within nebulous environments of shifting core elements – not entirely under the control of the individual. Learning (defined as actionable knowledge) can reside outside of ourselves (within an organization or a database), is focused on connecting specialized information sets, and the connections that enable us to learn more are more important than our current state of knowing.

Principles of connectivism:

  • Learning and knowledge rests in diversity of opinions.
  • Learning is a process of connecting specialized nodes or information sources.
  • Learning may reside in non-human appliances
  • Capacity to know more is more critical than what is currently known
  • Nurturing and maintaining connections is needed to facilitate continual learning.
  • Ability to see connections between fields, ideas, and concepts is a core skill.
  • Currency (accurate, up-to-date knowledge) is the intent of all connectivist learning activities.
  • Decision-making is itself a learning process. Choosing what to learn and the meaning of incoming information is seen through the lens of a shifting reality. While there is a right answer now, it may be wrong tomorrow due to alterations in the information climate affecting the decision.

    The table below indicates how prominent learning theories differ from connectivism:

    How learning occurs
    Black box—observable behaviour main focus
    Structured, computational
    Social, meaning created by each learner (personal)
    Distributed within a network, social, technologically enhanced, recognizing and interpreting patterns
    Influencing factors
    Nature of reward, punishment, stimuli
    Existing schema, previous experiences
    Engagement, participation, social, cultural
    Diversity of network, strength of ties, context of occurrence 
    Role of memory
    Memory is the hardwiring of repeated experiences—where reward and punishment are most influential
    Encoding, storage, retrieval
    Prior knowledge remixed to current context
    Adaptive patterns, representative of current state, existing in networks
    How transfer occurs
    Stimulus, response
    Duplicating knowledge constructs of “knower”
    Connecting to (adding) nodes and growing the network (social/conceptual/biological)
    Types of learning best explained
    Task-based learning
    Reasoning, clear objectives, problem solving
    Social, vague
    (“ill defined”)
    Complex learning, rapid changing core, diverse knowledge sources

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